Computer Science > Machine Learning
[Submitted on 24 Jun 2022 (v1), last revised 23 Feb 2023 (this version, v3)]
Title:Robustness to corruption in pre-trained Bayesian neural networks
View PDFAbstract:We develop ShiftMatch, a new training-data-dependent likelihood for robustness to corruption in Bayesian neural networks (BNNs). ShiftMatch is inspired by the training-data-dependent "EmpCov" priors from Izmailov et al. (2021a), and efficiently matches test-time spatial correlations to those at training time. Critically, ShiftMatch is designed to leave the neural network's training time likelihood unchanged, allowing it to use publicly available samples from pre-trained BNNs. Using pre-trained HMC samples, ShiftMatch gives strong performance improvements on CIFAR-10-C, outperforms EmpCov priors (though ShiftMatch uses extra information from a minibatch of corrupted test points), and is perhaps the first Bayesian method capable of convincingly outperforming plain deep ensembles.
Submission history
From: Xi Wang [view email][v1] Fri, 24 Jun 2022 16:08:46 UTC (175 KB)
[v2] Thu, 29 Sep 2022 13:51:40 UTC (270 KB)
[v3] Thu, 23 Feb 2023 14:39:05 UTC (313 KB)
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